A risk-ranking methodology for prioritizing historic, potentially contaminated mine sites in British Columbia
Bibliographic record
Abstract
The Crown Land Restoration Branch (CLRB) of the British Columbia Ministry of Agriculture and Lands is responsible for managing thousands of historic and abandoned mine sites on provincial lands (referred to as Crown Contaminated Sites). For most of these sites, there is limited information available regarding the extent of potential contamination or potential human health and ecological risks. Given the large number of sites, the CLRB sought a system for prioritizing investigation and management efforts among them. We developed a Risk-Ranking Methodology (RRM) to meet this objective, which was implemented in 2007/2008 with an emphasis on historic mine sites because of the significant number of sites and related potential risk. The RRM uses a risk-based Preliminary Site Investigation to gather key information about the sites. The information for each site is analyzed and summarized according to several attributes aimed at characterizing potential health and ecological risks. The summary information includes, but is not limited to, generic comparisons of exposure with effects levels (screening quotients) for human and ecological exposure pathways. The summary information (more than 25 attributes) is then used in a workshop setting to evaluate relative rankings among sites, and also to identify subsequent management actions for each site. Application of the RRM in 2007/2008 was considered successful, because there was confidence in the process, the content and the outputs. A key challenge was keeping the number of attributes to a manageable level. Ranking was based on discussion and consensus, which was a feasible approach given the relatively small number of sites that need to be ranked each year, and facilitated transparency in the ranking process. We do not rule out the future possibility of developing a quantitative function to capture trade-offs among attributes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".